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Fitting a bivariate measurement error model for episodically consumed dietary components.

Saijuan Zhang1, Susan M Krebs-Smith, Douglas Midthune

  • 1Texas A&M University, TX, USA.

The International Journal of Biostatistics
|August 1, 2012
PubMed
Summary
This summary is machine-generated.

Estimating usual dietary intake, especially energy-adjusted intake of episodically consumed foods like fish and whole grains, is crucial for public health. This study introduces a faster, flexible Monte Carlo method for modeling such dietary data.

Keywords:
Bayesian approachlatent variablesmeasurement errormixed effects modelsnutritional epidemiologyzero-inflated data

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Area of Science:

  • Nutritional Epidemiology
  • Statistical Modeling
  • Public Health Nutrition

Background:

  • Estimating usual long-term dietary intake of episodically consumed foods (e.g., fish, whole grains) is vital for public health.
  • Short-term dietary measurements often exhibit zero-inflated, skewed distributions, necessitating specialized statistical models.
  • Existing models correct for measurement error but often focus on univariate intake, not energy-adjusted intake, which reflects dietary composition.

Purpose of the Study:

  • To develop and evaluate a novel computational method for fitting nonlinear mixed-effects models for energy-adjusted usual dietary intake.
  • To address limitations of maximum likelihood fitting, such as slow computation and convergence issues.
  • To provide a flexible modeling approach applicable to various episodically consumed dietary components.

Main Methods:

  • Developed a Monte Carlo (Markov Chain Monte Carlo) computation for fitting a nonlinear mixed-effects model.
  • The model addresses patterned covariance matrices, a technical challenge in statistical modeling.
  • Applied the method to the NIH-AARP Diet and Health Study, modeling energy-adjusted usual intake of fish and whole grains.

Main Results:

  • The Monte Carlo method significantly increases computational speed compared to traditional maximum likelihood fitting.
  • The developed methods demonstrate convergence to reasonable statistical solutions.
  • The approach offers flexibility for both frequentist and Bayesian statistical inference.

Conclusions:

  • The Monte Carlo approach provides an efficient and reliable method for modeling energy-adjusted usual dietary intake of episodically consumed foods.
  • This advancement is crucial for accurate nutritional epidemiology and public health assessments.
  • The method's flexibility enhances its applicability across diverse dietary studies.